Making Sense of Unstructured Data

Structured data only accounts for about 20 percent of stored information. The rest is unstructured data – includes texts, blogs, documents, photos, videos, etc. In this presentation, I will talk about *analytical methods and tools, to analyze unstructured data, *that data scientists may use to gather and analyze information that doesn’t have a pre-defined model or structure. Traditional analytical processes are not adequate to fully understand unstructured data an as such, I want to dwell on some of the newer methods such as semantic analysis and natural language processing to analyze unstructured data. I will talk about the best practices that has worked for me in my quest to untangle unstructured data as well as do shallow dives into Recurrent Neural Networks (RNN) and Convolutional Neural Networks and how deep learning is helping at identifying patterns in unstructured data.

I am an Analytics Leader with 15+ years of experience in driving business solution through innovation, growth hacks and thought leadership in analytics technology/product domain. My work in Predictive Analytics, Informatics, and Visualization is widely recognized in the industry and I have been invited to conferences and top tier universities to speak on topics such as Growth Hacking, Big Data, Machine Learning, Artificial Intelligence and Data Visualization. I am also an active peer reviewer for top machine learning and artificial intelligence conferences and journals. Over the last 10 years, I have worked on launching mega-successful products at Amazon and Facebook.